docstr: Add a new dataset to the Database.
  
  Args:
      train_path (str):
          Path to the training CSV file. It can be a local filesystem path,
          absolute or relative, or an HTTP or HTTPS URL, or an S3 path in the
          format ``s3://{bucket_name}/{key}``. Required.
      test_path (str):
          Path to the testing CSV file. It can be a local filesystem path,
          absolute or relative, or an HTTP or HTTPS URL, or an S3 path in the
          format ``s3://{bucket_name}/{key}``.
          Optional. If not given, the training CSV will be split in two parts,
          train and test.
      name (str):
          Name given to this dataset. Optional. If not given, a hash will be
          generated from the training_path and used as the Dataset name.
      description (str):
          Human friendly description of the Dataset. Optional.
      class_column (str):
          Name of the column that will be used as the target variable.
          Optional. Defaults to ``'class'``.
  
  Returns:
      Dataset:
          The created dataset.
docstr_tok: ["Add","a","new","dataset","to","the","Database",".","Args",":","train_path","(","str",")",":","Path","to","the","training","CSV","file",".","It","can","be","a","local","filesystem","path",",","absolute","or","relative",",","or","an","HTTP","or","HTTPS","URL",",","or","an","S3","path","in","the","format","``","s3",":","\/\/","{","bucket_name","}","\/","{","key","}","``",".","Required",".","test_path","(","str",")",":","Path","to","the","testing","CSV","file",".","It","can","be","a","local","filesystem","path",",","absolute","or","relative",",","or","an","HTTP","or","HTTPS","URL",",","or","an","S3","path","in","the","format","``","s3",":","\/\/","{","bucket_name","}","\/","{","key","}","``",".","Optional",".","If","not","given",",","the","training","CSV","will","be","split","in","two","parts",",","train","and","test",".","name","(","str",")",":","Name","given","to","this","dataset",".","Optional",".","If","not","given",",","a","hash","will","be","generated","from","the","training_path","and","used","as","the","Dataset","name",".","description","(","str",")",":","Human","friendly","description","of","the","Dataset",".","Optional",".","class_column","(","str",")",":","Name","of","the","column","that","will","be","used","as","the","target","variable",".","Optional",".","Defaults","to","``","'class","'","``",".","Returns",":","Dataset",":","The","created","dataset","."]
code: def add_dataset(
      self,
      train_path,
      test_path=None,
      name=None,
      description=None,
      class_column=None,
  ):
      """Add a new dataset to the Database.
  
      Args:
          train_path (str):
              Path to the training CSV file. It can be a local filesystem path,
              absolute or relative, or an HTTP or HTTPS URL, or an S3 path in the
              format ``s3://{bucket_name}/{key}``. Required.
          test_path (str):
              Path to the testing CSV file. It can be a local filesystem path,
              absolute or relative, or an HTTP or HTTPS URL, or an S3 path in the
              format ``s3://{bucket_name}/{key}``.
              Optional. If not given, the training CSV will be split in two parts,
              train and test.
          name (str):
              Name given to this dataset. Optional. If not given, a hash will be
              generated from the training_path and used as the Dataset name.
          description (str):
              Human friendly description of the Dataset. Optional.
          class_column (str):
              Name of the column that will be used as the target variable.
              Optional. Defaults to ``'class'``.
  
      Returns:
          Dataset:
              The created dataset.
      """
  
      return self.db.create_dataset(
          train_path=train_path,
          test_path=test_path,
          name=name,
          description=description,
          class_column=class_column,
          aws_access_key=self.aws_access_key,
          aws_secret_key=self.aws_secret_key,
      )
  
file_path: ATM/atm/core.py
imports: import logging
  import random
  import time
  from datetime import datetime, timedelta
  from operator import attrgetter
  from tqdm import tqdm
  from atm.constants import TIME_FMT, PartitionStatus, RunStatus
  from atm.database import Database
  from atm.method import Method
  from atm.worker import ClassifierError, Worker
label_desc: Use image node_id 2 for calling the ATM obj's underlying member method code with example usage: obj.add_dataset(train_path, test_path, name, description, class_column) and returns: self